Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Tuesday 09 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 138,889 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 138,879 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 61
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 44
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 35
## 67 2020-05-06 East of England 30
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 24
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 16
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 15
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 13
## 95 2020-06-03 East of England 9
## 96 2020-06-04 East of England 4
## 97 2020-06-05 East of England 4
## 98 2020-06-06 East of England 3
## 99 2020-06-07 East of England 1
## 100 2020-03-01 London 0
## 101 2020-03-02 London 0
## 102 2020-03-03 London 0
## 103 2020-03-04 London 0
## 104 2020-03-05 London 0
## 105 2020-03-06 London 1
## 106 2020-03-07 London 1
## 107 2020-03-08 London 0
## 108 2020-03-09 London 1
## 109 2020-03-10 London 0
## 110 2020-03-11 London 7
## 111 2020-03-12 London 6
## 112 2020-03-13 London 10
## 113 2020-03-14 London 14
## 114 2020-03-15 London 10
## 115 2020-03-16 London 17
## 116 2020-03-17 London 25
## 117 2020-03-18 London 31
## 118 2020-03-19 London 25
## 119 2020-03-20 London 45
## 120 2020-03-21 London 50
## 121 2020-03-22 London 54
## 122 2020-03-23 London 64
## 123 2020-03-24 London 87
## 124 2020-03-25 London 113
## 125 2020-03-26 London 130
## 126 2020-03-27 London 130
## 127 2020-03-28 London 122
## 128 2020-03-29 London 147
## 129 2020-03-30 London 150
## 130 2020-03-31 London 181
## 131 2020-04-01 London 202
## 132 2020-04-02 London 190
## 133 2020-04-03 London 196
## 134 2020-04-04 London 230
## 135 2020-04-05 London 195
## 136 2020-04-06 London 198
## 137 2020-04-07 London 219
## 138 2020-04-08 London 238
## 139 2020-04-09 London 205
## 140 2020-04-10 London 170
## 141 2020-04-11 London 176
## 142 2020-04-12 London 158
## 143 2020-04-13 London 166
## 144 2020-04-14 London 143
## 145 2020-04-15 London 142
## 146 2020-04-16 London 139
## 147 2020-04-17 London 99
## 148 2020-04-18 London 101
## 149 2020-04-19 London 102
## 150 2020-04-20 London 95
## 151 2020-04-21 London 95
## 152 2020-04-22 London 108
## 153 2020-04-23 London 77
## 154 2020-04-24 London 71
## 155 2020-04-25 London 57
## 156 2020-04-26 London 53
## 157 2020-04-27 London 51
## 158 2020-04-28 London 43
## 159 2020-04-29 London 44
## 160 2020-04-30 London 39
## 161 2020-05-01 London 41
## 162 2020-05-02 London 40
## 163 2020-05-03 London 36
## 164 2020-05-04 London 29
## 165 2020-05-05 London 25
## 166 2020-05-06 London 36
## 167 2020-05-07 London 37
## 168 2020-05-08 London 29
## 169 2020-05-09 London 23
## 170 2020-05-10 London 26
## 171 2020-05-11 London 18
## 172 2020-05-12 London 18
## 173 2020-05-13 London 16
## 174 2020-05-14 London 20
## 175 2020-05-15 London 18
## 176 2020-05-16 London 14
## 177 2020-05-17 London 15
## 178 2020-05-18 London 9
## 179 2020-05-19 London 13
## 180 2020-05-20 London 19
## 181 2020-05-21 London 12
## 182 2020-05-22 London 10
## 183 2020-05-23 London 6
## 184 2020-05-24 London 7
## 185 2020-05-25 London 8
## 186 2020-05-26 London 12
## 187 2020-05-27 London 7
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## 191 2020-05-31 London 6
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## 195 2020-06-04 London 6
## 196 2020-06-05 London 2
## 197 2020-06-06 London 0
## 198 2020-06-07 London 0
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## 200 2020-03-02 Midlands 0
## 201 2020-03-03 Midlands 1
## 202 2020-03-04 Midlands 0
## 203 2020-03-05 Midlands 0
## 204 2020-03-06 Midlands 0
## 205 2020-03-07 Midlands 0
## 206 2020-03-08 Midlands 3
## 207 2020-03-09 Midlands 1
## 208 2020-03-10 Midlands 0
## 209 2020-03-11 Midlands 2
## 210 2020-03-12 Midlands 6
## 211 2020-03-13 Midlands 5
## 212 2020-03-14 Midlands 4
## 213 2020-03-15 Midlands 5
## 214 2020-03-16 Midlands 11
## 215 2020-03-17 Midlands 8
## 216 2020-03-18 Midlands 13
## 217 2020-03-19 Midlands 8
## 218 2020-03-20 Midlands 28
## 219 2020-03-21 Midlands 13
## 220 2020-03-22 Midlands 31
## 221 2020-03-23 Midlands 33
## 222 2020-03-24 Midlands 41
## 223 2020-03-25 Midlands 48
## 224 2020-03-26 Midlands 64
## 225 2020-03-27 Midlands 72
## 226 2020-03-28 Midlands 89
## 227 2020-03-29 Midlands 92
## 228 2020-03-30 Midlands 90
## 229 2020-03-31 Midlands 123
## 230 2020-04-01 Midlands 140
## 231 2020-04-02 Midlands 142
## 232 2020-04-03 Midlands 124
## 233 2020-04-04 Midlands 151
## 234 2020-04-05 Midlands 164
## 235 2020-04-06 Midlands 140
## 236 2020-04-07 Midlands 123
## 237 2020-04-08 Midlands 186
## 238 2020-04-09 Midlands 139
## 239 2020-04-10 Midlands 127
## 240 2020-04-11 Midlands 142
## 241 2020-04-12 Midlands 139
## 242 2020-04-13 Midlands 120
## 243 2020-04-14 Midlands 116
## 244 2020-04-15 Midlands 147
## 245 2020-04-16 Midlands 102
## 246 2020-04-17 Midlands 118
## 247 2020-04-18 Midlands 115
## 248 2020-04-19 Midlands 92
## 249 2020-04-20 Midlands 107
## 250 2020-04-21 Midlands 86
## 251 2020-04-22 Midlands 78
## 252 2020-04-23 Midlands 103
## 253 2020-04-24 Midlands 79
## 254 2020-04-25 Midlands 72
## 255 2020-04-26 Midlands 81
## 256 2020-04-27 Midlands 74
## 257 2020-04-28 Midlands 68
## 258 2020-04-29 Midlands 53
## 259 2020-04-30 Midlands 56
## 260 2020-05-01 Midlands 64
## 261 2020-05-02 Midlands 51
## 262 2020-05-03 Midlands 52
## 263 2020-05-04 Midlands 61
## 264 2020-05-05 Midlands 58
## 265 2020-05-06 Midlands 59
## 266 2020-05-07 Midlands 48
## 267 2020-05-08 Midlands 34
## 268 2020-05-09 Midlands 37
## 269 2020-05-10 Midlands 41
## 270 2020-05-11 Midlands 33
## 271 2020-05-12 Midlands 45
## 272 2020-05-13 Midlands 39
## 273 2020-05-14 Midlands 37
## 274 2020-05-15 Midlands 40
## 275 2020-05-16 Midlands 34
## 276 2020-05-17 Midlands 31
## 277 2020-05-18 Midlands 34
## 278 2020-05-19 Midlands 34
## 279 2020-05-20 Midlands 36
## 280 2020-05-21 Midlands 32
## 281 2020-05-22 Midlands 27
## 282 2020-05-23 Midlands 32
## 283 2020-05-24 Midlands 19
## 284 2020-05-25 Midlands 25
## 285 2020-05-26 Midlands 33
## 286 2020-05-27 Midlands 29
## 287 2020-05-28 Midlands 27
## 288 2020-05-29 Midlands 20
## 289 2020-05-30 Midlands 20
## 290 2020-05-31 Midlands 20
## 291 2020-06-01 Midlands 20
## 292 2020-06-02 Midlands 20
## 293 2020-06-03 Midlands 21
## 294 2020-06-04 Midlands 11
## 295 2020-06-05 Midlands 17
## 296 2020-06-06 Midlands 7
## 297 2020-06-07 Midlands 3
## 298 2020-03-01 North East and Yorkshire 0
## 299 2020-03-02 North East and Yorkshire 0
## 300 2020-03-03 North East and Yorkshire 0
## 301 2020-03-04 North East and Yorkshire 0
## 302 2020-03-05 North East and Yorkshire 0
## 303 2020-03-06 North East and Yorkshire 0
## 304 2020-03-07 North East and Yorkshire 0
## 305 2020-03-08 North East and Yorkshire 0
## 306 2020-03-09 North East and Yorkshire 0
## 307 2020-03-10 North East and Yorkshire 0
## 308 2020-03-11 North East and Yorkshire 0
## 309 2020-03-12 North East and Yorkshire 0
## 310 2020-03-13 North East and Yorkshire 0
## 311 2020-03-14 North East and Yorkshire 0
## 312 2020-03-15 North East and Yorkshire 2
## 313 2020-03-16 North East and Yorkshire 3
## 314 2020-03-17 North East and Yorkshire 1
## 315 2020-03-18 North East and Yorkshire 2
## 316 2020-03-19 North East and Yorkshire 6
## 317 2020-03-20 North East and Yorkshire 5
## 318 2020-03-21 North East and Yorkshire 6
## 319 2020-03-22 North East and Yorkshire 7
## 320 2020-03-23 North East and Yorkshire 9
## 321 2020-03-24 North East and Yorkshire 8
## 322 2020-03-25 North East and Yorkshire 18
## 323 2020-03-26 North East and Yorkshire 21
## 324 2020-03-27 North East and Yorkshire 28
## 325 2020-03-28 North East and Yorkshire 35
## 326 2020-03-29 North East and Yorkshire 38
## 327 2020-03-30 North East and Yorkshire 64
## 328 2020-03-31 North East and Yorkshire 60
## 329 2020-04-01 North East and Yorkshire 67
## 330 2020-04-02 North East and Yorkshire 74
## 331 2020-04-03 North East and Yorkshire 100
## 332 2020-04-04 North East and Yorkshire 105
## 333 2020-04-05 North East and Yorkshire 92
## 334 2020-04-06 North East and Yorkshire 96
## 335 2020-04-07 North East and Yorkshire 102
## 336 2020-04-08 North East and Yorkshire 107
## 337 2020-04-09 North East and Yorkshire 111
## 338 2020-04-10 North East and Yorkshire 117
## 339 2020-04-11 North East and Yorkshire 98
## 340 2020-04-12 North East and Yorkshire 84
## 341 2020-04-13 North East and Yorkshire 94
## 342 2020-04-14 North East and Yorkshire 107
## 343 2020-04-15 North East and Yorkshire 96
## 344 2020-04-16 North East and Yorkshire 103
## 345 2020-04-17 North East and Yorkshire 88
## 346 2020-04-18 North East and Yorkshire 95
## 347 2020-04-19 North East and Yorkshire 88
## 348 2020-04-20 North East and Yorkshire 100
## 349 2020-04-21 North East and Yorkshire 76
## 350 2020-04-22 North East and Yorkshire 84
## 351 2020-04-23 North East and Yorkshire 62
## 352 2020-04-24 North East and Yorkshire 72
## 353 2020-04-25 North East and Yorkshire 69
## 354 2020-04-26 North East and Yorkshire 65
## 355 2020-04-27 North East and Yorkshire 65
## 356 2020-04-28 North East and Yorkshire 57
## 357 2020-04-29 North East and Yorkshire 69
## 358 2020-04-30 North East and Yorkshire 57
## 359 2020-05-01 North East and Yorkshire 64
## 360 2020-05-02 North East and Yorkshire 48
## 361 2020-05-03 North East and Yorkshire 40
## 362 2020-05-04 North East and Yorkshire 49
## 363 2020-05-05 North East and Yorkshire 40
## 364 2020-05-06 North East and Yorkshire 50
## 365 2020-05-07 North East and Yorkshire 45
## 366 2020-05-08 North East and Yorkshire 42
## 367 2020-05-09 North East and Yorkshire 44
## 368 2020-05-10 North East and Yorkshire 40
## 369 2020-05-11 North East and Yorkshire 29
## 370 2020-05-12 North East and Yorkshire 27
## 371 2020-05-13 North East and Yorkshire 28
## 372 2020-05-14 North East and Yorkshire 30
## 373 2020-05-15 North East and Yorkshire 32
## 374 2020-05-16 North East and Yorkshire 35
## 375 2020-05-17 North East and Yorkshire 26
## 376 2020-05-18 North East and Yorkshire 29
## 377 2020-05-19 North East and Yorkshire 27
## 378 2020-05-20 North East and Yorkshire 21
## 379 2020-05-21 North East and Yorkshire 33
## 380 2020-05-22 North East and Yorkshire 22
## 381 2020-05-23 North East and Yorkshire 18
## 382 2020-05-24 North East and Yorkshire 23
## 383 2020-05-25 North East and Yorkshire 21
## 384 2020-05-26 North East and Yorkshire 21
## 385 2020-05-27 North East and Yorkshire 18
## 386 2020-05-28 North East and Yorkshire 19
## 387 2020-05-29 North East and Yorkshire 24
## 388 2020-05-30 North East and Yorkshire 20
## 389 2020-05-31 North East and Yorkshire 18
## 390 2020-06-01 North East and Yorkshire 15
## 391 2020-06-02 North East and Yorkshire 22
## 392 2020-06-03 North East and Yorkshire 21
## 393 2020-06-04 North East and Yorkshire 15
## 394 2020-06-05 North East and Yorkshire 12
## 395 2020-06-06 North East and Yorkshire 18
## 396 2020-06-07 North East and Yorkshire 4
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## 412 2020-03-16 North West 2
## 413 2020-03-17 North West 4
## 414 2020-03-18 North West 6
## 415 2020-03-19 North West 7
## 416 2020-03-20 North West 10
## 417 2020-03-21 North West 11
## 418 2020-03-22 North West 13
## 419 2020-03-23 North West 16
## 420 2020-03-24 North West 21
## 421 2020-03-25 North West 21
## 422 2020-03-26 North West 29
## 423 2020-03-27 North West 35
## 424 2020-03-28 North West 28
## 425 2020-03-29 North West 46
## 426 2020-03-30 North West 67
## 427 2020-03-31 North West 52
## 428 2020-04-01 North West 86
## 429 2020-04-02 North West 96
## 430 2020-04-03 North West 95
## 431 2020-04-04 North West 98
## 432 2020-04-05 North West 102
## 433 2020-04-06 North West 100
## 434 2020-04-07 North West 133
## 435 2020-04-08 North West 127
## 436 2020-04-09 North West 119
## 437 2020-04-10 North West 117
## 438 2020-04-11 North West 138
## 439 2020-04-12 North West 126
## 440 2020-04-13 North West 127
## 441 2020-04-14 North West 131
## 442 2020-04-15 North West 114
## 443 2020-04-16 North West 134
## 444 2020-04-17 North West 98
## 445 2020-04-18 North West 113
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## 447 2020-04-20 North West 83
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## 449 2020-04-22 North West 86
## 450 2020-04-23 North West 85
## 451 2020-04-24 North West 66
## 452 2020-04-25 North West 65
## 453 2020-04-26 North West 55
## 454 2020-04-27 North West 54
## 455 2020-04-28 North West 57
## 456 2020-04-29 North West 62
## 457 2020-04-30 North West 59
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## 459 2020-05-02 North West 56
## 460 2020-05-03 North West 55
## 461 2020-05-04 North West 48
## 462 2020-05-05 North West 48
## 463 2020-05-06 North West 44
## 464 2020-05-07 North West 49
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## 466 2020-05-09 North West 30
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## 469 2020-05-12 North West 38
## 470 2020-05-13 North West 24
## 471 2020-05-14 North West 26
## 472 2020-05-15 North West 33
## 473 2020-05-16 North West 32
## 474 2020-05-17 North West 24
## 475 2020-05-18 North West 30
## 476 2020-05-19 North West 35
## 477 2020-05-20 North West 26
## 478 2020-05-21 North West 25
## 479 2020-05-22 North West 26
## 480 2020-05-23 North West 31
## 481 2020-05-24 North West 26
## 482 2020-05-25 North West 31
## 483 2020-05-26 North West 27
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## 486 2020-05-29 North West 19
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## 489 2020-06-01 North West 12
## 490 2020-06-02 North West 25
## 491 2020-06-03 North West 17
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## 531 2020-04-05 South East 82
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## 533 2020-04-07 South East 100
## 534 2020-04-08 South East 83
## 535 2020-04-09 South East 104
## 536 2020-04-10 South East 88
## 537 2020-04-11 South East 88
## 538 2020-04-12 South East 88
## 539 2020-04-13 South East 84
## 540 2020-04-14 South East 65
## 541 2020-04-15 South East 72
## 542 2020-04-16 South East 56
## 543 2020-04-17 South East 86
## 544 2020-04-18 South East 57
## 545 2020-04-19 South East 70
## 546 2020-04-20 South East 85
## 547 2020-04-21 South East 50
## 548 2020-04-22 South East 54
## 549 2020-04-23 South East 57
## 550 2020-04-24 South East 64
## 551 2020-04-25 South East 51
## 552 2020-04-26 South East 51
## 553 2020-04-27 South East 40
## 554 2020-04-28 South East 40
## 555 2020-04-29 South East 47
## 556 2020-04-30 South East 29
## 557 2020-05-01 South East 37
## 558 2020-05-02 South East 36
## 559 2020-05-03 South East 17
## 560 2020-05-04 South East 35
## 561 2020-05-05 South East 29
## 562 2020-05-06 South East 25
## 563 2020-05-07 South East 27
## 564 2020-05-08 South East 26
## 565 2020-05-09 South East 28
## 566 2020-05-10 South East 19
## 567 2020-05-11 South East 25
## 568 2020-05-12 South East 27
## 569 2020-05-13 South East 18
## 570 2020-05-14 South East 32
## 571 2020-05-15 South East 24
## 572 2020-05-16 South East 22
## 573 2020-05-17 South East 17
## 574 2020-05-18 South East 22
## 575 2020-05-19 South East 12
## 576 2020-05-20 South East 22
## 577 2020-05-21 South East 14
## 578 2020-05-22 South East 17
## 579 2020-05-23 South East 19
## 580 2020-05-24 South East 16
## 581 2020-05-25 South East 13
## 582 2020-05-26 South East 17
## 583 2020-05-27 South East 17
## 584 2020-05-28 South East 12
## 585 2020-05-29 South East 17
## 586 2020-05-30 South East 8
## 587 2020-05-31 South East 10
## 588 2020-06-01 South East 11
## 589 2020-06-02 South East 9
## 590 2020-06-03 South East 15
## 591 2020-06-04 South East 9
## 592 2020-06-05 South East 8
## 593 2020-06-06 South East 4
## 594 2020-06-07 South East 0
## 595 2020-03-01 South West 0
## 596 2020-03-02 South West 0
## 597 2020-03-03 South West 0
## 598 2020-03-04 South West 0
## 599 2020-03-05 South West 0
## 600 2020-03-06 South West 0
## 601 2020-03-07 South West 0
## 602 2020-03-08 South West 0
## 603 2020-03-09 South West 0
## 604 2020-03-10 South West 0
## 605 2020-03-11 South West 1
## 606 2020-03-12 South West 0
## 607 2020-03-13 South West 0
## 608 2020-03-14 South West 1
## 609 2020-03-15 South West 0
## 610 2020-03-16 South West 0
## 611 2020-03-17 South West 2
## 612 2020-03-18 South West 2
## 613 2020-03-19 South West 5
## 614 2020-03-20 South West 3
## 615 2020-03-21 South West 6
## 616 2020-03-22 South West 9
## 617 2020-03-23 South West 9
## 618 2020-03-24 South West 7
## 619 2020-03-25 South West 9
## 620 2020-03-26 South West 11
## 621 2020-03-27 South West 13
## 622 2020-03-28 South West 21
## 623 2020-03-29 South West 18
## 624 2020-03-30 South West 23
## 625 2020-03-31 South West 23
## 626 2020-04-01 South West 22
## 627 2020-04-02 South West 23
## 628 2020-04-03 South West 30
## 629 2020-04-04 South West 42
## 630 2020-04-05 South West 32
## 631 2020-04-06 South West 34
## 632 2020-04-07 South West 39
## 633 2020-04-08 South West 47
## 634 2020-04-09 South West 24
## 635 2020-04-10 South West 46
## 636 2020-04-11 South West 43
## 637 2020-04-12 South West 23
## 638 2020-04-13 South West 27
## 639 2020-04-14 South West 24
## 640 2020-04-15 South West 32
## 641 2020-04-16 South West 29
## 642 2020-04-17 South West 33
## 643 2020-04-18 South West 25
## 644 2020-04-19 South West 31
## 645 2020-04-20 South West 26
## 646 2020-04-21 South West 26
## 647 2020-04-22 South West 22
## 648 2020-04-23 South West 17
## 649 2020-04-24 South West 19
## 650 2020-04-25 South West 15
## 651 2020-04-26 South West 27
## 652 2020-04-27 South West 13
## 653 2020-04-28 South West 17
## 654 2020-04-29 South West 15
## 655 2020-04-30 South West 26
## 656 2020-05-01 South West 6
## 657 2020-05-02 South West 7
## 658 2020-05-03 South West 10
## 659 2020-05-04 South West 16
## 660 2020-05-05 South West 14
## 661 2020-05-06 South West 19
## 662 2020-05-07 South West 16
## 663 2020-05-08 South West 6
## 664 2020-05-09 South West 11
## 665 2020-05-10 South West 5
## 666 2020-05-11 South West 8
## 667 2020-05-12 South West 7
## 668 2020-05-13 South West 7
## 669 2020-05-14 South West 6
## 670 2020-05-15 South West 4
## 671 2020-05-16 South West 4
## 672 2020-05-17 South West 6
## 673 2020-05-18 South West 4
## 674 2020-05-19 South West 6
## 675 2020-05-20 South West 1
## 676 2020-05-21 South West 9
## 677 2020-05-22 South West 6
## 678 2020-05-23 South West 6
## 679 2020-05-24 South West 3
## 680 2020-05-25 South West 8
## 681 2020-05-26 South West 11
## 682 2020-05-27 South West 5
## 683 2020-05-28 South West 9
## 684 2020-05-29 South West 4
## 685 2020-05-30 South West 3
## 686 2020-05-31 South West 2
## 687 2020-06-01 South West 6
## 688 2020-06-02 South West 2
## 689 2020-06-03 South West 5
## 690 2020-06-04 South West 1
## 691 2020-06-05 South West 1
## 692 2020-06-06 South West 1
## 693 2020-06-07 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Monday 08 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -7.8069 -1.8581 0.2118 1.8383 4.5717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.107e+00 5.157e-02 99.02 <2e-16 ***
## note_lag 1.033e-05 4.946e-07 20.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.638648)
##
## Null deviance: 3629.28 on 37 degrees of freedom
## Residual deviance: 283.14 on 36 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 165.12736 1.00001
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 149.104684 182.515882
## note_lag 1.000009 1.000011
Rsq(lag_mod)
## [1] 0.9219845
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
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## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
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## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
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## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18
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## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
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## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
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## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
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## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
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